MLOps-Enabled Autonomous AI Agents for End-to-End Software Project Management

Shawaiz Arif; Muhammad Ahmed

Authors

Shawaiz Arif Faculty of Computer Science & Information Technology, Superior University, Pakistan
Muhammad Ahmed Faculty of Computer Science & Information Technology, Superior University, Pakistan

Keywords:

Autonomous AI Agents, MLOps, Software Project Management, Task Scheduling, Risk Assessment, Resource Optimization, Agile Development, Predictive Analytics, Multi-Agent Systems, Explainable AI

Abstract

The rapid evolution of software project management (SPM) practices necessitates adaptive, intelligent systems capable of managing complex projects efficiently. This paper presents an MLOps-enabled framework of autonomous AI agents for end-to-end software project management, integrating task scheduling, predictive risk assessment, resource optimization, and automated stakeholder communication. The framework leverages multi-agent coordination and continuous MLOps pipelines to ensure reliable, adaptive, and scalable decision-making. Experimental evaluation across 90 simulated and historical software projects within a controlled Discrete Event Simulation environment demonstrates significant improvements, including a 64.98% reduction in average task delays, an 89.06% reduction in critical risk incidents, a 31.8% improvement in resource utilization consistency, and a 67.2% improvement in stakeholder response time compared to conventional Critical Path Method (CPM) and heuristic approaches. Overall Task Completion Efficiency (TCE) reached 92.5% (95% CI: 91.2%–93.8%) and Risk Mitigation Effectiveness (RME) reached 89.3% (95% CI: 87.6%–91.0%) across 50 independent simulation runs. Per-sprint analysis reveals progressive performance improvement from Sprint 1 (TCE: 88.2%; RME: 85.1%; Resource Utilization: 79.3%; Stakeholder Satisfaction Index: 4.2/5.0) to Sprint 5 (TCE: 94.7%; RME: 91.2%; RU: 87.2%; SSI: 4.7/5.0), demonstrating the compounding benefits of continuous MLOps retraining. Inter-agent communication overhead averaged 4.2 ms per message (95% CI: 3.8–4.6 ms), confirming negligible coordination latency. Model drift, measured via Population Stability Index (PSI), decreased from 0.21 to 0.08 across five automated retraining cycles triggered after every fifth simulation sprint, with zero agent downtime during retraining events. These outcomes were achieved through automated model retraining, drift detection, and explainable decision support for human-in-the-loop oversight. These simulation-based results highlight the framework's potential to redefine project management, providing a strong empirical foundation for future real-world validation in enterprise software development environments.

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